Deep learning has recently been extensively investigated to remove artifacts in low-dose computed tomography (LDCT). However, the power of transfer learning for medical image denoising tasks has not been fully explored. In this work, we proposed a transfer learning residual convolutional neural network (TLR-CNN) to restore LDCT images at single and blind noise levels. A residual network was implemented to effectively estimate the difference between denoised image and its original map, and a noise-free image was obtained by subtracting the residual map from the LDCT image. The results were compared to competing baseline denoising methods in terms of quantitative metrics including the PSNR, RMSE, SSIM and FSIM. For the single noise level, the proposed method demonstrated better denoising performance than the other algorithms for both simulation data and clinical data. As for the blind denoising, the image qualities were improved for all noise levels for all the quantitative metrics, but such improvements were decreasing as the noise level decrease (higher mAs). Comparative experiments suggested that the proposed network could effectively suppress artifacts and preserve image details with faster converge rate and reduced computational time.

Uniform large-area flat-panel X-ray sources have potential applications in projection imaging. Achieving such a source has remained a challenge. A diagonal 4-in cold cathode flat-panel X-ray source device has been fabricated using ZnO nanowire field emitter arrays. A large-area uniform X-ray emission was produced, and projection imaging with a resolution of more than 5.0 lp/mm was realized. Its projection imaging resolution and its dependence on geometric imaging parameter settings were studied along with the imaging performance of the device. Simulations using the fast ray-tracing algorithm were performed, yielding results consistent with those from experiments. Optimal parameter settings for high-resolution projection imaging using the flat-panel X-ray source are presented. This study has significance in the application of this cold cathode flat-panel X-ray source in X-ray imaging.

Purpose of Review Myocardial perfusion imaging (MPI) with SPECT is performed clinically worldwide to detect and monitor coronary artery disease (CAD). MPI allows an objective quantification of myocardial perfusion at stress and rest. This established technique relies on normal databases to compare patient scans against reference normal limits. In this review, we aim to introduce the process of MPI quantification with normal databases and describe the associated perfusion quantitative measures that are used. Recent Findings New equipment and new software reconstruction algorithms have been introduced, which require the development of new normal limits. The appearance and regional count variations of normal MPI scans may differ between these new scanners and standard Anger cameras. Therefore, these new systems may require the determination of new normal limits to achieve optimal accuracy in relative myocardial perfusion quantification. Accurate diagnostic and prognostic results rivaling those obtained by expert readers can be obtained by this widely used technique. Summary Throughout this review, we emphasize the importance of the normal databases and the need for specific databases relative to distinct imaging procedures. Use of appropriate normal limits allows optimal quantification of MPI by taking into account subtle image differences due to the hardware and software used, and the population studied.

A fully vacuum-sealed addressable flat-panel X-ray source based on ZnO nanowire field emitter arrays (FEAs) was fabricated. The device has a diode structure composed of cathode panel and anode panel. ZnO nanowire cold cathodes were prepared on strip electrodes on a cathode panel and Mo thin film strips were prepared on an anode panel acting as the target. Localized X-ray emission was realized by cross-addressing of cathode and anode electrodes. A radiation dose rate of 10.8 μGy/s was recorded at the anode voltage of 32 kV. The X-ray imaging of objects using different addressing scheme was obtained and the imaging results were analyzed. The results demonstrated the feasibility of achieving addressable flat-panel X-ray source using diode-structure for advanced X-ray imaging.